Predicting the dose of vancomycin in ICU patients receiving different types of RRT therapy: a model‐based meta‐analytic approach. Issue 6 (7th April 2019)

• Record Type: Journal Article
• Authors: Claisse, Guillaume; Zufferey, Paul J.; Trone, Jane C.; Maillard, Nicolas; Delavenne, Xavier; Laporte, Silvy; Ollier, Edouard
• Is Part Of: British journal of clinical pharmacology. Volume 85:Issue 6(2019)
• Page Start: 1215
• Page End: 1226
• Publication Date: 2019-04-07
• Subjects: Clearance -- Intensive Care -- Model Based Meta‐Analysis -- Pharmacokinetics -- Renal Replacement Therapy -- Vancomycin; Pharmacology -- Periodicals; Drugs -- Periodicals; 615.1
• DOI: 10.1111/bcp.13904
• Languages: English
• ISSNs: 0306-5251

Abstract

Abstract: Aim: Previous pharmacokinetic (PK) studies have proposed various dosing regimens for vancomycin in intensive care unit (ICU) patients undergoing renal replacement therapy (RRT), but all are restricted to specific RRT modalities. To be useful in practice, a population PK model would need to predict vancomycin clearance during any RRT modality. Development of such a model is feasible using meta‐analysis of published summarized estimates of vancomycin PK parameters. Our aims were: (i) to develop and validate a population PK model for vancomycin that takes into account any RRT modalities, and (ii) to predict vancomycin dosing for RRT patients in ICU. Methods: Vancomycin pharmacokinetics were assumed to be two‐compartmental, total body clearance being the sum of non‐RRT clearance and RRT‐induced clearance. Drug disposition and non‐RRT clearance parameters were estimated by systematic review and meta‐analysis of previously published parameter estimates. The relationship between RRT‐induced clearance and RRT flowrate settings was assessed using a model‐based meta‐analysis. Prediction performances of the PK model were assessed using external data. Results: The meta‐analyses of disposition parameters, non‐RRT clearance and RRT‐induced clearance included 11, 6 and 38 studies (84 RRT clearance measurements) respectively. The model performed well in predicting external individual PK data. Individual vancomycin concentrations during RRT were accurately predicted using Bayesian estimation based solely on pre‐RRT measurements. Conclusions: The PK model allowed accurate prediction of the vancomycin pharmacokinetics during RRT in ICU patients. Based on the model of RRT‐induced clearance, an appropriate adjustment of the vancomycin dosing regimen could be proposed for any kind of flowrate settings.